, 2009; Lai et al., 2005; Leenders et al., 2008). Olaparib Accordingly, we conducted cell-surface biotinylation assays to examine whether Cdk5/p35 increases CaV2.2 surface expression in our heterologous system. However, CaV2.2 surface expression was not upregulated by Cdk5/p35 in stable cell lines or after coexpression with WT CaV2.2 or 8X CaV2.2, indicating that altered channel surface expression was not responsible for the enhancement of the CaV2.2 current density by Cdk5/p35-mediated phosphorylation (Figures 4A and 4B; Table S3). We next assessed channel open probability (Po) of CaV2.2 in the presence of Cdk5/p35 as previously

described (Agler et al., 2005). To obtain maximal channel open probability, Po, for each cell, the maximal ionic current conductance, Gmax (Figure 4C), was plotted as a function of the integral of the channel gating current at the reversal potential Qmax (Figure 4D). Interestingly, Cdk5/p35-mediated phosphorylation

of CaV2.2 increased the channel open probability, LBH589 Po (Figures 4E and 4F). Importantly, the Cdk5/p35-mediated increase in the WT CaV2.2 channel open probability was not observed in 8X CaV2.2. To further determine whether the dramatic increase in CaV2.2 current density impacts CaV2.2 surface expression in primary neurons, we cloned the full-length WT CaV2.2 α1 subunit or the 8X CaV2.2 α1 subunit cDNA into a bicistronic herpes simplex virus (HSV) backbone that coexpresses green fluorescent protein (GFP) (Neve et al., 2005). In primary neurons, transduction with HSV yields about 90% GFP-positive cells after 24 hr (Figure S4A). Upon transduction of primary neurons with WT CaV2.2 or 8X CaV2.2 HSV, however, there were no alterations in CaV2.2 surface levels compared to neurons transduced 17-DMAG (Alvespimycin) HCl with control GFP HSV (Figure S4B). Collectively, these data suggest that in addition to increased channel availability, Cdk5-mediated phosphorylation of CaV2.2 results in increased calcium influx due to enhanced channel open probability. We predicted that Cdk5-mediated phosphorylation of CaV2.2 might play an important physiological role in CaV2.2-mediated neurotransmission. To test this hypothesis,

whole-cell CaV2.2 currents were isolated in neurons transduced with HSV expressing control GFP, WT CaV2.2, or 8X CaV2.2. Consistent with our heterologous cell data, Cdk5-mediated phosphorylation of WT CaV2.2, but not 8X CaV2.2, increased neuronal CaV2.2 current density when compared to neurons expressing control GFP HSV (Figure 5A; Table S4). Furthermore, inhibition of Cdk5 activity using a dominant-negative Cdk5 (DNK5) HSV further reduced CaV2.2 current density (Figure 5A), suggesting that Cdk5 is the major kinase responsible for CaV2.2 phosphorylation and increased CaV2.2 current density. We also examined whether the P/Q-type calcium channel (CaV2.1), the other major presynaptic calcium channel in neurons, was affected by expression of WT CaV2.2 or 8X CaV2.2 HSV but found no differences in CaV2.

“
“Neurodegenerative

brain diseases are collectively characterized by two core features: abnormal protein deposition and distinctive profiles of damage across the brain and over time (Frisoni et al., 2010 and Rohrer et al., 2011). If we understood in detail how proteinopathies translate to clinical phenotypes, we might anticipate and perhaps prevent the devastating impact of these diseases. While we have recognized for some time that spatiotemporal brain atrophy profiles track neuropathological patterns of disease evolution (Frisoni et al., 2010), we have lacked a principled framework for understanding and predicting the profiles observed. The Dolutegravir brain is composed of neural networks and graph theory provides a methodology for representing and analyzing those networks (Bullmore

and Sporns, 2009). Work in animal models has demonstrated a correspondence between mathematically derived network characteristics and the hierarchical and distributed www.selleckchem.com/products/z-vad-fmk.html architectures of neuroanatomy (Modha and Singh, 2010). Network-level analysis is an ideal approach to understanding neurodegenerative diseases, due both to the fundamentally coherent and distributed nature of the underlying pathological processes and the failure of conventional approaches to adequately explain the distinctive phenomenology of these diseases. However, the potential clinical value of network-based approaches remains largely unrealized. Two papers in this issue of Neuron ( Raj et al., 2012 and Zhou et al., 2012) take us further toward this goal, by applying the methods of graph theory to quantify and predict network disintegration in

a range of neurodegenerative diseases. These papers capitalize on two key recent insights: the expression of neurodegeneration within specific, distributed, intrinsic brain networks ( Zhou et al., 2010) and the propensity of culprit proteins to “template” further protein aggregation and spread of disease along neural pathways ( Hardy, 2005 and de Calignon et al., 2012). Raj et al. (2012) model network diffusion based on tractography data in the healthy brain and not derive robust spatial eigenmodes that correspond closely to atrophy profiles observed in Alzheimer’s disease and frontotemporal dementia; their model makes no prior assumptions about selective neuronal vulnerabilities or protein-specific factors. Zhou et al. (2012) show that common neurodegeneration syndromes seed distinctive connectivity structures derived using task-free fMRI in the healthy brain: their data suggest that the neurodegenerative process spreads primarily between neurons according to the functional proximity of specific brain regions acting as critical hub-like “epicenters,” rather than various alternative candidate mechanisms. Both papers agree that transsynaptic diffusion plays a core role in the spread of neurodegenerative pathologies, and together they provide a succinct framework for characterizing network disintegration in these diseases.

However, in one study perceptual learning decreased the slope of the function relating BOLD to pitch-interval size in microtonal stimuli (Zatorre et al., in press). Such specific reduction to a particular feature suggests ABT-263 that the outcome of learning

under some circumstances may be that fewer neuronal units are needed to encode a given level of information, as also suggested for visual perceptual learning (Yotsumoto et al., 2008). Findings of specific adaptations within a sensory system raise the question of the behavioral relevance and transfer to other, related tasks. However, pitch discrimination training for instance does not necessarily lead to improved vocal performance or associated neural changes (Zarate et al., 2010). Thus, transfer from sensory to motor domains cannot be assumed. It is important then to ask how active musical training that involves producing sound influences sensory responses and more generally what its effects are on the entire sensory-motor system. Several recent studies have looked at training that involves actively playing a musical instrument and that therefore

involves the sensorimotor system in addition to the auditory system. Many studies on the effects of instrumental musical training are cross-sectional in nature, comparing groups of musicians Afatinib order and nonmusicians; since here we are mostly interested in training studies, we will emphasize those that pertain most to the results of later training studies. For example, musicians show enlarged auditory cortical evoked potentials to piano tones (Pantev et al., 1998), and this effect can be additionally modulated according to the timbre of their own musical instrument (Pantev et al.,

2001), much especially in the right auditory cortex (Shahin et al., 2003). Complementary fMRI findings were reported when comparing violinists and flutists (Margulis et al., 2009), where an experience-specific network encompassed auditory associations areas related to timbre processing, and also precentral and inferior frontal areas involved in auditory-motor interactions and in musical syntax processing, respectively. More recently, instrument-specific tuning has been demonstrated as early as the brainstem level (Strait et al., 2012). Such instrument-specific effects provide good evidence for experience-dependent plasticity. The effects of experience have been tested more directly in longitudinal studies that followed children taking instrumental lessons with the Suzuki method. The Suzuki method is particularly suited for systematic studies because it is standardized, because no preselection of students based on inherent talent takes place, and because the training focuses on playing by ear and learning by imitation. Although some studies have not provided conclusive proof for specific training effects in evoked electrical responses (Shahin et al.

In addition, the results of Tanzi and colleagues suggest a potential therapeutic approach

to increasing adult neurogenesis in AD by raising sAPPα levels via gene therapy or direct infusion of sAPPα into the hippocampus. Finally, the current study strengthens the causal link between abnormal Aβ metabolism and LOAD. The hypothesis that LOAD is caused by cerebral Aβ accumulation has been controversial, in part because rare highly penetrant LOAD mutations that affect Aβ metabolism have not been found, unlike the case for EO-FAD. Now, the rare highly penetrant LOAD mutations in the ADAM10 prodomain strongly support the conclusion that there is no qualitative difference between Selleckchem Dinaciclib EO-FAD and LOAD and that they share a similar disease mechanism involving early cerebral Aβ accumulation but that they quantitatively differ in onset and severity depending on the rate of Aβ accumulation. In other words, EO-FAD and LOAD are the same disease but reside in different regions along a pathogenic continuum. As such, the work of Tanzi and colleagues represents an important paradigm shift in the field and further supports the amyloid cascade hypothesis of AD. “
“Alzheimer’s

disease (AD) is pathologically characterized Y-27632 purchase by the presence of both extracellular Aβ deposits and intracellular deposits of tau in the brain. Multiple lines Bay 11-7085 of evidence indicate that accumulation and aggregation of both proteins plays a pivotal role in disease, and thus both Aβ and tau have been the primary foci of efforts to develop disease-modifying therapies for AD. Tau inclusion pathology also is the primary pathological hallmark of several other neurodegenerative disorders such as progressive supranuclear palsy and Pick’s disease. In addition, mutations

in tau that result in neurofibrillary pathology and neurodegeneration can cause FTLD-17t. Thus, tau is a major therapeutic target in AD and in neurodegenerative diseases that are collectively referred to as tauopathies. Following the pioneering preclinical studies by Schenk and colleagues demonstrating the preclinical efficacy of active and passive immunotherapy targeting Aβ, there has been increasing interest in developing immunotherapies to treat AD and other neurodegenerative proteinopathies including human tauopathies. Though many questions remain regarding mechanisms of action of anti-Aβ immunotherapies and optimal trial design to evaluate efficacy in humans, there has been rapid advancement of these therapies into human trials (Golde et al., 2009), although initial therapeutic trials have not shown significant evidence for efficacy in humans with mild to moderate AD (Golde et al., 2011).

, 2003). Importantly, memory retrieval through these modified Vorinostat cost KC-output synapses was predicted to guide either odor avoidance or approach behavior. A KC synapse-specific representation of memories of opposing valence would dictate that it is not possible to functionally separate the retrieval of aversive and appetitive memories by disrupting KC-wide processes. We therefore tested these models by systematically blocking neurotransmission from subsets of the retrieval-relevant

αβ neurons. We found that aversive and appetitive memories can be distinguished in the αβ KC population, showing that opposing odor memories do not exclusively rely on overlapping KCs. Whereas output from the αβs neurons is required for aversive and appetitive memory retrieval, the αβ core (αβc) neurons are only critical for conditioned approach behavior. Higher-resolution anatomical analysis of the innervation

of reinforcing DA neurons suggests that valence-specific asymmetry may be established during training. Furthermore, dendrites of KC-output neurons differentially innervate the MB in a similarly stratified manner. We therefore propose that aversive memories are retrieved and avoidance behavior triggered only from the αβ surface (αβs) Palbociclib manufacturer neurons, whereas appetitive memories are retrieved and approach behavior is driven by efferent neurons that integrate across the αβ ensemble. Several studies have reported the

importance of output from MB αβ neurons for the retrieval of aversive and appetitive olfactory memories (Dubnau et al., 2001, McGuire et al., 2001, Schwaerzel et al., 2003, Krashes et al., 2007, Krashes and Waddell, 2008 and Trannoy Levetiracetam et al., 2011). However, genetic labeling reveals further anatomical segregation of the ∼1,000 αβ neurons into at least αβ posterior (αβp or pioneer), αβ surface (αβs or early), and αβ core (αβc or late) subsets that are sequentially born during development (Ito et al., 1997, Lee et al., 1999 and Tanaka et al., 2008). We therefore investigated the role of these αβ subsets in memory retrieval. We first obtained, or identified, GAL4 lines with expression that was restricted to αβ subsets and verified their expression. Prior reports showed that the c739 GAL4 (McGuire et al., 2001) labels αβ neurons contributing to all three classes (Aso et al., 2009). In contrast, NP7175 expresses in αβc neurons and c708a in αβp neurons (Murthy et al., 2008, Tanaka et al., 2008 and Lin et al., 2007). Lastly, we identified the 0770 GAL4 line from the InSITE collection (Gohl et al., 2011) with strong expression in αβs neurons and weaker expression in αβp neurons. We expressed a membrane-tethered GFP (uas-mCD8::GFP) using the c739, 0770, NP7175, and c708a GAL4 drivers and localized expression within the overall MB neurons using a LexAop-rCD2::RFP transgene driven by 247-LexA::VP16 (Pitman et al., 2011).

We also detected PRT expression in the peduncle, formed by KC axons before they branch into the lobes (Figures 3D–3F). PRT was not distributed uniformly throughout the peduncle, and a portion of the core was weakly labeled (Figures 3D–3F; data not shown). This pattern suggests that PRT may not be expressed in all KCs, although further experiments will be needed to confirm this. Several additional cell bodies near the MBs express PRT (Figures 3A–3C and 3F), as well as one cluster of two to three cells in the subesophageal ganglion that projects medially Bortezomib toward

the esophogeal foramen (arrows, Figure 3F). During metamorphosis there is also extensive development of the central complex, a midline structure just posterior to the MB medial lobes involved in motor activity (Strauss, 2002) and visual memory (Liu et al., 2006). PRT labeling of the adult brain revealed that it is expressed in components of the CCX, including the neuropil of the ellipsoid and fan-shaped bodies (Figures 3G and 3H). We also detected PRT expression in two bilaterally symmetric clusters of two and three cells, each near the medial aspect of the optic lobe, that project outward toward the medulla (asterisks, Figure 3I). The cartoon in Figure 3J summarizes the PRT expressing cells in the adult. Pifithrin-�� mw Other than the KCs, there are approximately 56 labeled

cell bodies. For comparison, the adult brain contains approximately 300 dopaminergic and 106 serotonergic cells (Monastirioti, 1999). To complete our survey of the adult central nervous system, we also labeled the thoracic ganglion and found three clusters with two to four cells each that lie near the ventral midline ALOX15 (Figures 3K–3M). This expression pattern was not sexually dimorphic (Figures 3L and 3M). To investigate the function of PRT, we generated a mutant

fly. A survey of the public database revealed a previously generated line with a SUPor-P element inserted into the 5′ untranslated region (UTR) of prt ( Figure 4A). Line KG07780 was obtained from the Bloomington Stock Center (Indiana University), and we confirmed that the SUPor-P element was located 118 bp upstream of the predicted initiating methionine (data not shown). We used imprecise excision to generate a prt mutation. Lines were screened by PCR, with primers flanking the P element insertion. In wild-type Canton-S (CS) flies, we detected a major product that migrated at 1.2 kb, consistent with the size predicted by the primary sequence ( Figure 4B). In one line, the major band migrated at 400 bp, consistent with an 850 bp deletion ( Figure 4B). We designated this allele prt1. We immunolabeled adult brains to determine whether prt1 mutants produce any residual protein, and we failed to detect any labeling of the MBs or elsewhere ( Figure 4C). These data confirm the specificity of the antiserum to PRT.

, 1995) and observed an ∼47 kDa protein in brain lysates Ibrutinib from each SCA7-CTCF-I-mut transgenic line (Figure 3C). We noted higher expression in the SCA7-CTCF-I-mut-(2) line, consistent with its more severe phenotype. Low-level expression of the ∼47 kDa protein was detected in SCA7-CTCF-I-wt mice (Figure 3C),

and this ∼47 kDa protein product corresponds to an open reading frame starting at the initiator ATG codon in exon 3 and continuing through exon 4 until the first nonsense codon in intron 4. The production of a protein product and disease phenotype in the SCA7-CTCF-I-mut mice is reminiscent of the R6/2 mouse model of HD, in which a small fragment from the htt gene was introduced into mice to model repeat Stem Cell Compound Library cost instability, but also yielded a truncated protein product resulting in a HD-like phenotype—despite the fact that the construct lacked a 3′ polyadenylation site or characterized

promoter (Mangiarini et al., 1996). Our findings indicate that mutation of the 3′ CTCF binding site is responsible for initiation of robust sense transcription in SCA7-CTCF-mut-I mice, as SCA7-CTCF-I-wt mice carrying an ataxin-7 genomic fragment with an intact 3′ CTCF binding site express low levels of ataxin-7 mRNA and protein. To determine if the levels of ataxin-7 sense and antisense transcription within the repeat region domain correlate in SCA7-CTCF-I-wt and SCA7-CTCF-I-mut mice, we performed quantitative strand-specific RT-PCR amplification, and detected ataxin-7 sense and antisense transcripts in each line. We found that

ataxin-7 sense transcript levels were elevated ∼370-fold in the brains of SCA7-CTCF-I-mut mice compared to SCA7-CTCF-I-wt mice, and this was accompanied by an ∼140-fold decrease in SCAANT1 expression (Figure 3D). In situ hybridization analysis confirmed robust expression of SCAANT1 in the cerebellum of SCA7-CTCF-I-wt mice but did not detect strong SCAANT1 expression in SCA7-CTCF-I-mut mice (Figure 3E). In situ hybridization analysis indicated moderate Cediranib (AZD2171) to strong expression of SCAANT1 in SCA7-CTCF-I-wt mice throughout the brain (Figure S4A). Correspondingly, in situ hybridization did not yield evidence for much SCAANT1 expression in the brain of SCA7-CTCF-I-mut mice (Figure S4B). Taken together, these findings show that reduced SCAANT1 expression correlates with increased P2A promoter activity, resulting in increased sense expression of the ataxin-7 gene. Our studies of the SCA7-CTCF-I-wt and SCA7-CTCF-I-mut mice suggested that expression of the ataxin-7 sense transcript inversely correlates with expression of SCAANT1. To determine if this reciprocal expression relationship exists in normal human tissues, we performed qRT-PCR analysis on a panel of human tissue RNAs.

Elegantly, such a scheme could regulate the energy supplied to neurons in response to their activity, since glutamate released by active neurons could promote lactate production in astrocytes

by stimulating glycolytic ATP generation to power astrocytic uptake of glutamate and its conversion to glutamine. CX-5461 manufacturer Neuronal activity does elevate lactate levels in the brain (Prichard et al., 1991), some studies (but not others) show that lactate can replace glucose as a power source for neurons (Schurr et al., 1988; Allen et al., 2005; Wyss et al., 2011), and lactate transporters are found in postsynaptic spines where most neuronal ATP is used (Bergersen et al., 2005). However, the extent to which astrocytes “feed” neurons, and even the direction of any lactate flux between the two cell types, remain controversial (Jolivet et al., 2010; Mangia et al., 2011). Consequently, a demonstration that

long-term potentiation and memory are disrupted by deletion of lactate transporters (Suzuki et al., 2011; Newman et al., 2011) might reflect a signaling role for lactate, rather than an energetic one. Indeed, one way in which lactate may provide synapses with energy is by being employed as a prostaglandin-modulating messenger to increase blood flow (Gordon et al., 2008; Attwell et al., 2010). Synaptic activity is far from constant and changes dramatically NLG919 solubility dmso on time scales from seconds to days. How then is ATP production by synaptic mitochondria regulated to match this demand? We will consider short-term regulation of energy supply in this section, and long-term regulation below. When ATP is consumed pre- and postsynaptically by the processes shown in Figure 1, the resulting increase of [ADP]/[ATP]

will, by the law of mass action, tend to increase ATP formation by oxidative phosphorylation (Chance and Williams, 1955). However, the rise of [Ca2+]i that occurs presynaptically to control transmitter release and postsynaptically at synapses expressing found NMDA receptors (or Ca2+-permeable AMPA receptors) provides another stimulus increasing ATP production rapidly in response to synaptic activity (Chouhan et al., 2012; see Gellerich et al. [2010] for a review and Mathiesen et al. [2011] for an opposing view). The rise of [Ca2+]i leads to a rise of mitochondrial [Ca2+]i, which activates mitochondrial dehydrogenases that promote citric acid cycle activity (Duchen, 1992). The rise of cytoplasmic [Ca2+]i also activates the mitochondrial aspartate-glutamate exchanger aralar (Gellerich et al., 2009), which raises [NADH] in mitochondria and thus supports H+ pumping out across the mitochondrial membrane and subsequent ATP synthesis. Antiapoptotic Bcl2 family proteins may also regulate ATP production by decreasing ion leak through the F1FO ATP synthase (Alavian et al., 2011). Activity-evoked entry of Ca2+ into synaptic mitochondria buffers the cytoplasmic [Ca2+]i rise occurring (Billups and Forsythe, 2002).

For each session a tetrode, consisting of four platinum/tungsten core channels embedded in a quartzite probe

with a triangular/center configuration (Thomas Recording imped: 500 KΩ–1.4 MΩ) was inserted through a stainless steel guide tube positioned in a grid system (Crist Instruments) within the recording chamber. The recording tip of the tetrode was physiologically monitored as it was driven down to target by a microdrive (Nan Drives, Israel). Continuous LFP recordings were drawn from one of four tetrode channels. Signals were preamplified with unity gain (Plexon headstage), and then amplified PD0332991 manufacturer (5,000–20,000 X), and bandpassed (0.7 Hz–170 Hz) using the Plexon Muti-channel Acquisition Processor (MAP) system. Signals were digitized at 1 KHz, and saved to disk for offline analysis. Offline analyses were conducted using MATLAB scripts developed

for the current project, and incorporated the Chronux toolbox (P. Mitra at Cold Spring Harbor Laboratories). The LFP signals for each session were separated into 4 s sweeps coinciding with the trial onsets and offsets. Individual LFP sweeps were inspected for noise and artifacts that saturated the amplifiers, with the sweeps that violated these criteria being removed from further analyses. Sixty hertz line noise was digitally removed using Butterworth filters (MATLAB signal processing toolbox). Each sweep was converted LBH589 price into an individual spectra of frequency and power across the 4 s duration, using five discrete prolate spheroidal sequence (DPSS) data

tapers applied to a 300 ms sliding window, stepped at 50 ms intervals, giving a 10 Hz aggregate resolution. Specific trial type comparisons of the LFP spectra were made for the nonoverlapping spectra bandwidths of gamma (30–100 Hz) and beta (10–25 Hz) across predetermined epochs based on the previous single unit findings of Wirth et al., 2003 and Wirth et al., 2009. Human BOLD fMRI data were pooled from two studies that employed the same conditional-motor-associative learning task for a total of 31 subjects (Kirwan et al., 2007 and Law et al., 2005). Subjects were solicited from the John Hopkins community and paid for Org 27569 their participation. Thirteen of the subjects were male, 18 were female, and all subjects were right handed with a mean age of 26.7 years (range 18–33). Imaging data were collected using a Phillips 3.0 Tesla scanner (Best, The Netherlands) equipped with a SENSE (sensitivity encoding) head coil. Functional echoplanar images were collected via a high-speed single-shot pulse sequence with an 80 × 80 acquisition matrix size, a 30 ms echo time, a 70° flip angle, a SENSE factor of 2, and a 3 × 3 mm in-plane acquisition resolution. Two acquisitions per trial for 132 trials per run made for a total of 264 whole-brain three-dimensional volumes that were acquired with a repetition time (TR) of 1.5 s for each run.

During its 250 ms cycle, the 4 Hz Panobinostat binaural beat stimulus traverses all possible combinations of ipsi- and contralateral phase, allowing a two-dimensional representation of the subthreshold input as a function of both monaural phases (Figure 3D).

The horizontal and vertical ridges in this graph reveal the phase locking of the binaural subthreshold response to the ipsi- and contralateral tone, respectively. The crossing point of these ridges combines the favored phases of both ears, and the peak created by this combination of monaural phases is where one expects the eAPs. The actual timing of eAPs (white dots in Figure 3D) was slightly offset relative to the peak. The direction and magnitude of this offset represents an average latency of 158 μs between peak subthreshold input and APs, consistent with the average EPSP-AP latency of this recording of 173 μs. Thus, Figure 3D shows that subthreshold responses predicted ITD tuning well. Binaural tuning of the subthreshold input was further analyzed by determining, for each value of IPD, the peak potential of the portions of the recording corresponding to that IPD, (i.e., the maximum across diagonal sections of

Figure 3D). The IPD-dependence of this peak potential is shown in Figure 3A (green line) along with the cycle histogram of eAPs. Again, www.selleckchem.com/products/i-bet151-gsk1210151a.html the binaural tuning of the spikes matches the binaural tuning of the subthreshold input quite well. Figure 3E compares measured best ITDs with predictions from the subthreshold input (as exemplified by the peak

of the green curve in Figure 3A) for all our recordings having significant (Rayleigh test, p < 0.001; 22 cells, including 3 cells recorded in whole-cell mode) binaural tuning. The correlation r = 0.84 confirms the predictability of binaural tuning from the monaural inputs. The shape of the cycle-averaged subthreshold inputs varied with stimulus frequency (Figures 4A and S5), higher frequencies yielding sinusoidal shapes similar to the intracellularly recorded subthreshold waveforms in nucleus laminaris cells of second the barn owl (Funabiki et al., 2011). Responses to low-frequency (<500 Hz) stimuli often showed multiple peaks per tone cycle (e.g., Figure 4A, 200/204 Hz responses). Analysis of SBC recordings previously recorded in our lab suggested that multiple peaks could already be present in individual inputs to the MSO neurons (Figure S6). Interestingly, the multiple peaks were often matched between the inputs of both ears (Figures 4A and S3). We also expanded the analysis of binaural tuning of the subthreshold input (green curve in Figure 3A) to multiple frequencies (Figure 4B). When displayed as contour plots (Figures 4C–4E), these data yield a binaural receptive field, in which the effects of stimulus frequency and interaural phase are combined.